Difficulty-skill balance does not affect engagement and enjoyment: A pre-registered study using AI-controlled difficulty

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How does the difficulty of a task affect people’s enjoyment and engagement? Intrinsic motivation and flow theories posit a ‘goldilocks’ optimum where task difficulty matches performer skill, yet current work is confounded by questionable measurement practices and lacks scalable methods to manipulate objective difficulty-skill ratios. We developed a 2-player tactical game test suite with an AI-controlled opponent that uses a variant of the Monte Carlo Tree Search algorithm to precisely manipulate difficulty-skill ratios. A pre-registered study (n=311) showed that our AI produced targeted difficulty-skill ratios without participants noticing the manipulation, yet different ratios had no significant impact on enjoyment or engagement. This indicates that difficulty-skill balance does not always affect engagement and enjoyment, but that games with AI-controlled difficulty provide a useful paradigm for rigorous future work on this issue